An improved fuzzy C-means clustering algorithm based on PSO

被引：50

作者：

Niu Q. ^{[1
]}

Huang X. ^{[1
]}

机构：

[1] School of Computer Science and Technology, China University of Mining and Technology, Xuzhou Jiangsu

来源：

Journal of Software | 2011年 / 6卷 / 05期

关键词：

Clustering; Constraint strategy; Fuzzy C means; Membership; Particle swarm algorithm;

D O I：

10.4304/jsw.6.5.873-879

中图分类号：

学科分类号：

摘要：

To deal with the problem of premature convergence of the fuzzy c-means clustering algorithm based on particle swarm optimization, which is sensitive to noise and less effective when handling the data set that dimensions greater than the number of samples, a novel fuzzy c-means clustering method based on the enhanced Particle Swarm Optimization algorithm is presented. Firstly, this approach distributes the memberships on the basis of the distance between the sample and cluster centers, making memberships meet the constraints of FCM. Then, optimization strategy is presented that the optimal particle can be guided to close the group effectively. The experimental results show the proposed method significantly improves the clustering effect of the PSO-based FCM that encoded in membership. © 2011 ACADEMY PUBLISHER.

引用

页码：873 / 879

页数：6

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